{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BroIDS 日志分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 数据预处理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "conn_raw = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'Raw' + os.sep * 2 + 'conn.log'\n",
    "dns_raw = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'Raw' + os.sep * 2 + 'dns.log'\n",
    "conn_csv = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'CSV' + os.sep * 2 + 'conn.csv'\n",
    "dns_csv = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'CSV' + os.sep * 2 + 'dns.csv'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 提取conn日志"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(conn_raw) as f:\n",
    "    conn_li = f.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 使用正则表达式对字段进行提取\n",
    "import re\n",
    "conn_keys = re.findall(r'\"([a-zA-Z0-9_@]+)[=\"]+[a-zA-Z0-9-./\"_]+[\"]?,', conn_li[0])\n",
    "conn_keys.remove('bro')\n",
    "conn_keys.append('collector_node_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn_values = []\n",
    "for li in conn_li:\n",
    "    row = []\n",
    "    for key in conn_keys:\n",
    "        regex = '\\\"' + key + '\\\"' + r'[=\"]+([a-zA-Z0-9-./_]+)[\"]?'\n",
    "        pattern = re.compile(regex)\n",
    "        res = re.findall(pattern, li)\n",
    "        if(res):\n",
    "            row.append(res[0])\n",
    "        else:\n",
    "            row.append('NaN')\n",
    "    conn_values.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "conn = pd.DataFrame(conn_values, columns=conn_keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.to_csv(conn_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 提取dns日志"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(dns_raw) as f:\n",
    "    dns_li = f.read().splitlines()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dns_values = []\n",
    "for li in dns_li:\n",
    "    dns_values.append(re.findall(r'\"[a-zA-Z0-9_@]+[=\"]+([a-zA-Z0-9-./_]+)[\",]?', li))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "dns = pd.DataFrame(dns_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "dns.to_csv(dns_csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 特征工程"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "conn_csv = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'CSV' + os.sep * 2 + 'conn.csv'\n",
    "conn = pd.read_csv(conn_csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             ts  resp_pkts        id.resp_h resp_l2_addr orig_l2_addr  \\\n0  1.583000e+12          0  255.255.255.255           ff           2c   \n1  1.583020e+12          0             ff02           33           70   \n2  1.583020e+12          0             ff02           33           84   \n3  1.583000e+12          0  255.255.255.255           ff           84   \n4  1.583000e+12          0  255.255.255.255           ff           84   \n\n                        source  orig_bytes  duration                 uid  \\\n0  /opt/bro/spool/bro/conn.log       756.0  0.000850  CU4J7R1P5y36Y7BxAd   \n1  /opt/bro/spool/bro/conn.log       436.0  0.000012  Clx9kQ1sIR07W6W9re   \n2  /opt/bro/spool/bro/conn.log       100.0  0.000012  COalC812AiW73FHkV9   \n3  /opt/bro/spool/bro/conn.log       756.0  0.000525  CsChNr4mTRcR9IP5Db   \n4  /opt/bro/spool/bro/conn.log       756.0  0.000529  ClbASW3eMko1oMgGT8   \n\n   LINE_ID  ...  missed_bytes id.resp_p resp_ip_bytes orig_pkts  history  \\\n0     1001  ...             0      5246             0         4        D   \n1     1001  ...             0      5246             0         2        D   \n2     1001  ...             0       547             0         2        D   \n3     1001  ...             0      5246             0         4        D   \n4     1001  ...             0      5246             0         4        D   \n\n   GROUP_ID      batchtime     @timestamp  id.orig_p  proto  \n0  sie866f9  2020-03-01T01  2020-03-01T01    61232.0    udp  \n1  sie866f9  2020-03-01T01  2020-03-01T01     5264.0    udp  \n2  sie866f9  2020-03-01T01  2020-03-01T01      546.0    udp  \n3  sie866f9  2020-03-01T01  2020-03-01T01    64570.0    udp  \n4  sie866f9  2020-03-01T01  2020-03-01T01    63137.0    udp  \n\n[5 rows x 30 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ts</th>\n      <th>resp_pkts</th>\n      <th>id.resp_h</th>\n      <th>resp_l2_addr</th>\n      <th>orig_l2_addr</th>\n      <th>source</th>\n      <th>orig_bytes</th>\n      <th>duration</th>\n      <th>uid</th>\n      <th>LINE_ID</th>\n      <th>...</th>\n      <th>missed_bytes</th>\n      <th>id.resp_p</th>\n      <th>resp_ip_bytes</th>\n      <th>orig_pkts</th>\n      <th>history</th>\n      <th>GROUP_ID</th>\n      <th>batchtime</th>\n      <th>@timestamp</th>\n      <th>id.orig_p</th>\n      <th>proto</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.583000e+12</td>\n      <td>0</td>\n      <td>255.255.255.255</td>\n      <td>ff</td>\n      <td>2c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>756.0</td>\n      <td>0.000850</td>\n      <td>CU4J7R1P5y36Y7BxAd</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>5246</td>\n      <td>0</td>\n      <td>4</td>\n      <td>D</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>61232.0</td>\n      <td>udp</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1.583020e+12</td>\n      <td>0</td>\n      <td>ff02</td>\n      <td>33</td>\n      <td>70</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>436.0</td>\n      <td>0.000012</td>\n      <td>Clx9kQ1sIR07W6W9re</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>5246</td>\n      <td>0</td>\n      <td>2</td>\n      <td>D</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>5264.0</td>\n      <td>udp</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.583020e+12</td>\n      <td>0</td>\n      <td>ff02</td>\n      <td>33</td>\n      <td>84</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>100.0</td>\n      <td>0.000012</td>\n      <td>COalC812AiW73FHkV9</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>547</td>\n      <td>0</td>\n      <td>2</td>\n      <td>D</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>546.0</td>\n      <td>udp</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1.583000e+12</td>\n      <td>0</td>\n      <td>255.255.255.255</td>\n      <td>ff</td>\n      <td>84</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>756.0</td>\n      <td>0.000525</td>\n      <td>CsChNr4mTRcR9IP5Db</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>5246</td>\n      <td>0</td>\n      <td>4</td>\n      <td>D</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>64570.0</td>\n      <td>udp</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1.583000e+12</td>\n      <td>0</td>\n      <td>255.255.255.255</td>\n      <td>ff</td>\n      <td>84</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>756.0</td>\n      <td>0.000529</td>\n      <td>ClbASW3eMko1oMgGT8</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>5246</td>\n      <td>0</td>\n      <td>4</td>\n      <td>D</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>63137.0</td>\n      <td>udp</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 30 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 108
    }
   ],
   "source": [
    "conn.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "ts                 int64\nresp_pkts          int64\nid.resp_h         object\nresp_l2_addr      object\norig_l2_addr      object\nsource            object\norig_bytes       float64\nduration         float64\nuid               object\nLINE_ID            int64\nlocal_resp          bool\ncat               object\nconn_state        object\nhost              object\nlocal_orig          bool\nservice           object\noffset             int64\nid.orig_h         object\nresp_bytes       float64\norig_ip_bytes      int64\nmissed_bytes       int64\nid.resp_p          int64\nresp_ip_bytes      int64\norig_pkts          int64\nhistory           object\nGROUP_ID          object\nbatchtime         object\n@timestamp        object\nid.orig_p        float64\nproto             object\ndtype: object\n\n原始特征数: 30个\n"
    }
   ],
   "source": [
    "print(conn.dtypes)\n",
    "print('\\n原始特征数: {0}个'.format(len(conn.dtypes)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "ts                0\nresp_pkts         0\nid.resp_h         0\nresp_l2_addr      0\norig_l2_addr      0\nsource            0\norig_bytes       69\nduration         69\nuid               0\nLINE_ID           0\nlocal_resp        0\ncat               0\nconn_state        0\nhost              0\nlocal_orig        0\nservice           0\noffset            0\nid.orig_h         0\nresp_bytes       69\norig_ip_bytes     0\nmissed_bytes      0\nid.resp_p         0\nresp_ip_bytes     0\norig_pkts         0\nhistory           6\nGROUP_ID          0\nbatchtime         0\n@timestamp        1\nid.orig_p         1\nproto             0\ndtype: int64"
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "# 对缺失值进行处理\n",
    "conn.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn['orig_bytes'] = conn['orig_bytes'].fillna(0)\n",
    "conn['duration'] = conn['duration'].fillna(0)\n",
    "conn['resp_bytes'] = conn['resp_bytes'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = conn.dropna(subset=['id.orig_p'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "                ts  resp_pkts       id.resp_h resp_l2_addr orig_l2_addr  \\\n5    1583014184772          0            ff02           33           7c   \n266  1583021570131          0  129.103.84.215            0           7c   \n404  1583011365450          0  129.103.84.215            0           7c   \n627  1583023117179          0  129.103.84.215            0           7c   \n854  1582999443309          0  129.103.84.215            0           7c   \n983  1583011392227          0  129.103.84.215            0           7c   \n\n                          source  orig_bytes  duration                 uid  \\\n5    /opt/bro/spool/bro/conn.log        16.0  0.000017  CCmhWA3Gt3igBK7dEg   \n266  /opt/bro/spool/bro/conn.log       214.0  2.600429  CAG5Sy17AQkiR22awi   \n404  /opt/bro/spool/bro/conn.log         0.0  0.000000  CeustD4Ud0jXLBv7S1   \n627  /opt/bro/spool/bro/conn.log         0.0  0.000000  Cttqtp1Vx2PK1pZw53   \n854  /opt/bro/spool/bro/conn.log       476.0  4.929652  COYwrv3Vrp7pFXLKi9   \n983  /opt/bro/spool/bro/conn.log       476.0  4.933702   CkctZirOEHJlmgXlg   \n\n     LINE_ID  ...  missed_bytes id.resp_p resp_ip_bytes orig_pkts  history  \\\n5       1001  ...             0       134             0         2      NaN   \n266     1001  ...             0         3             0         2      NaN   \n404     1001  ...             0         3             0         1      NaN   \n627     1001  ...             0         3             0         1      NaN   \n854     1001  ...             0         3             0         2      NaN   \n983     1001  ...             0         3             0         2      NaN   \n\n     GROUP_ID      batchtime     @timestamp  id.orig_p  proto  \n5    sie866f9  2020-03-01T01  2020-03-01T01      133.0   icmp  \n266  sie866f9  2020-03-01T01  2020-03-01T01        3.0   icmp  \n404  sie866f9  2020-03-01T01  2020-03-01T01        3.0   icmp  \n627  sie866f9  2020-03-01T01  2020-03-01T01        3.0   icmp  \n854  sie866f9  2020-03-01T01  2020-03-01T01        3.0   icmp  \n983  sie866f9  2020-03-01T01  2020-03-01T01        3.0   icmp  \n\n[6 rows x 30 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ts</th>\n      <th>resp_pkts</th>\n      <th>id.resp_h</th>\n      <th>resp_l2_addr</th>\n      <th>orig_l2_addr</th>\n      <th>source</th>\n      <th>orig_bytes</th>\n      <th>duration</th>\n      <th>uid</th>\n      <th>LINE_ID</th>\n      <th>...</th>\n      <th>missed_bytes</th>\n      <th>id.resp_p</th>\n      <th>resp_ip_bytes</th>\n      <th>orig_pkts</th>\n      <th>history</th>\n      <th>GROUP_ID</th>\n      <th>batchtime</th>\n      <th>@timestamp</th>\n      <th>id.orig_p</th>\n      <th>proto</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>1583014184772</td>\n      <td>0</td>\n      <td>ff02</td>\n      <td>33</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>16.0</td>\n      <td>0.000017</td>\n      <td>CCmhWA3Gt3igBK7dEg</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>134</td>\n      <td>0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>133.0</td>\n      <td>icmp</td>\n    </tr>\n    <tr>\n      <th>266</th>\n      <td>1583021570131</td>\n      <td>0</td>\n      <td>129.103.84.215</td>\n      <td>0</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>214.0</td>\n      <td>2.600429</td>\n      <td>CAG5Sy17AQkiR22awi</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>3.0</td>\n      <td>icmp</td>\n    </tr>\n    <tr>\n      <th>404</th>\n      <td>1583011365450</td>\n      <td>0</td>\n      <td>129.103.84.215</td>\n      <td>0</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>CeustD4Ud0jXLBv7S1</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>3.0</td>\n      <td>icmp</td>\n    </tr>\n    <tr>\n      <th>627</th>\n      <td>1583023117179</td>\n      <td>0</td>\n      <td>129.103.84.215</td>\n      <td>0</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>0.0</td>\n      <td>0.000000</td>\n      <td>Cttqtp1Vx2PK1pZw53</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>1</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>3.0</td>\n      <td>icmp</td>\n    </tr>\n    <tr>\n      <th>854</th>\n      <td>1582999443309</td>\n      <td>0</td>\n      <td>129.103.84.215</td>\n      <td>0</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>476.0</td>\n      <td>4.929652</td>\n      <td>COYwrv3Vrp7pFXLKi9</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>3.0</td>\n      <td>icmp</td>\n    </tr>\n    <tr>\n      <th>983</th>\n      <td>1583011392227</td>\n      <td>0</td>\n      <td>129.103.84.215</td>\n      <td>0</td>\n      <td>7c</td>\n      <td>/opt/bro/spool/bro/conn.log</td>\n      <td>476.0</td>\n      <td>4.933702</td>\n      <td>CkctZirOEHJlmgXlg</td>\n      <td>1001</td>\n      <td>...</td>\n      <td>0</td>\n      <td>3</td>\n      <td>0</td>\n      <td>2</td>\n      <td>NaN</td>\n      <td>sie866f9</td>\n      <td>2020-03-01T01</td>\n      <td>2020-03-01T01</td>\n      <td>3.0</td>\n      <td>icmp</td>\n    </tr>\n  </tbody>\n</table>\n<p>6 rows × 30 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 104
    }
   ],
   "source": [
    "conn[conn.history.isnull() == True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "conn['label'] = 'normal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn_fix = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'Fix' + 'conn_fix.csv'\n",
    "conn.to_csv(conn_fix, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "conn_fix = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'Fix' + 'conn_fix.csv'\n",
    "conn = pd.read_csv(conn_fix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.ts = pd.to_datetime(conn.ts, unit='ms')\n",
    "conn.sort_values('ts', inplace=True)\n",
    "conn.reset_index(inplace=True, drop=True)\n",
    "conn.set_index('ts', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x150101b38>"
     },
     "metadata": {},
     "execution_count": 310
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('Resp Bytes')\n",
    "conn[5:].resp_bytes.plot(figsize=(8, 8), color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 模型构建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 无监督学习"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "conn_fix = os.getcwd() + os.sep * 2 + 'Data' + os.sep * 2 + 'Fix' + 'conn_fix.csv'\n",
    "conn = pd.read_csv(conn_fix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = conn[['id.resp_p', 'orig_bytes', 'resp_bytes', 'orig_ip_bytes', 'resp_pkts', 'resp_ip_bytes', 'duration']]\n",
    "X_test = X_train\n",
    "y = conn.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 452,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pyod.models.pca import PCA\n",
    "from pyod.models.feature_bagging import FeatureBagging\n",
    "from pyod.models.iforest import IForest\n",
    "from pyod.models.lof import LOF\n",
    "from pyod.models.knn import KNN\n",
    "\n",
    "clf = IForest(contamination=0.05)\n",
    "clf.fit(X_train)\n",
    "y_test_pred = clf.predict(X_test)\n",
    "y_test_scores = clf.decision_function(X_test)\n",
    "scores = pd.Series(y_test_scores)\n",
    "pred = pd.Series(y_test_pred)\n",
    "X_test['score'] = scores.values\n",
    "X_test['pred'] = pred.values\n",
    "conn['score'] = X_test['score']\n",
    "conn['pred'] = X_test['pred']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 454,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "正常数据: 952条\n异常数据: 47条\n"
    }
   ],
   "source": [
    "print('正常数据: {0}条\\n异常数据: {1}条'.format(len(conn[conn.pred == 0]), len(conn[conn.pred == 1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x176622080>"
     },
     "metadata": {},
     "execution_count": 467
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.title('Score Distribution')\n",
    "sns.distplot(conn.score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 453,
   "metadata": {},
   "outputs": [],
   "source": [
    "conn.label[conn.pred == 1] = 'abnormal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 455,
   "metadata": {},
   "outputs": [],
   "source": [
    "top10 = conn[conn.pred == 1].sort_values(by='score', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 457,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1763f49e8>"
     },
     "metadata": {},
     "execution_count": 457
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(16, 8))\n",
    "plt.title('Service Distribution')\n",
    "sns.countplot(hue='label', x='service', data=conn, palette='Set2')\n",
    "plt.legend(loc='upper right')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x175784240>"
     },
     "metadata": {},
     "execution_count": 458
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x432 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(16, 6))\n",
    "axes[0].set_title('Normal')\n",
    "axes[1].set_title('Abnormal')\n",
    "sns.kdeplot(conn.duration[conn.pred == 0], shade=True, color='g', ax=axes[0])\n",
    "sns.kdeplot(conn.duration[conn.pred == 1], shade=True, color='r', ax=axes[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 459,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x1758296d8>"
     },
     "metadata": {},
     "execution_count": 459
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 576x576 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "conn['id.resp_h'][conn.pred == 1].value_counts().plot.pie(autopct='%.1f%%', figsize=(8, 8), title='Destination IP Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 460,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x17581cb00>"
     },
     "metadata": {},
     "execution_count": 460
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 864x504 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 7))\n",
    "sns.countplot(y='id.resp_p'\n",
    ", hue='label', data=conn, color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 461,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x176318f60>"
     },
     "metadata": {},
     "execution_count": 461
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "conn.history[conn.pred == 1].value_counts().plot.pie(figsize=(6, 6), autopct='%.2f%%', colors=['gray', 'orange', 'yellow'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python37364bit9abb99062206432fbbc223d35be441c4",
   "display_name": "Python 3.7.3 64-bit"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}